Testing LIGER

# library(devtools)
# install_github('MacoskoLab/liger')
library(tidyverse)

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library(liger)
library(Rtsne)
library(reshape2)

Attaching package: ‘reshape2’

The following object is masked from ‘package:tidyr’:

    smiths

library(ggrepel)

Testing LIGER for integration of scATAC and scRNA data. Following vignette from (gitHub)[https://macoskolab.github.io/liger/walkthrough_rna_atac.html].

1. Download and load datasets:

ATAC data is preprocessed to gene-level features (no. of reads that overlap gene body and promoter for each gene). Data is downloaded from (here)[https://umich.app.box.com/s/5hkhpou4inrulo570yhc38ay8g3ont5b]

rna_clusts = readRDS("~/10X_data/rna_cluster_assignments.RDS")
atac_clusts = readRDS("~/10X_data/atac_cluster_assignments.RDS")
pbmc.atac <- readRDS('~/10X_data/pbmc.atac.expression.mat.RDS')
pbmc.rna <- readRDS('~/10X_data/pbmc.rna.expression.mat.RDS')

2. Create LIGER object

List of count matrices as input. createLiger converts to sparseMatrices.

pbmc.data = list(atac=pbmc.atac[,names(atac_clusts)], rna=pbmc.rna[,names(rna_clusts)])
int.pbmc <- createLiger(pbmc.data)

[1] "Removing 97 genes not expressing in atac."
[1] "Removing 11048 genes not expressing in rna."

3. Data preprocessing

## Normalize to column total (adj. for sequencing depth)
int.pbmc <- normalize(int.pbmc)
## Select highly variable genes ONLY IN THE RNA DATASET
int.pbmc <- selectGenes(int.pbmc, datasets.use = 2)
## Scale to the SD
int.pbmc <- scaleNotCenter(int.pbmc)

4. Run NMF

int.pbmc <- optimizeALS(int.pbmc, k=20)

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Converged in 3.941199 mins, 16 iterations.
Best results with seed 1.

smp <- sample(1:nrow(int.pbmc@H$atac), size = 1000)
smp_genes <- sample(1:length(int.pbmc@var.genes), size = 100)
int.pbmc@W[,smp_genes] %>% heatmap(Rowv = NA)

Error in int.pbmc@W[, smp_genes] %>% heatmap(Rowv = NA) : 
  could not find function "%>%"

Align factor

Dodgy quantile normalization

# saveRDS(int.pbmc, file = "~/10X_data/pbmc.trainedLIGER.RDS")
int.pbmc <- readRDS(int.pbmc, file = "~/10X_data/pbmc.trainedLIGER.RDS")

Error in readRDS(int.pbmc, file = "~/10X_data/pbmc.trainedLIGER.RDS") : 
  object 'int.pbmc' not found

int.pbmc@scale.data$atac[,
                         smp_genes] %>%
  melt(varnames=c("cell", "gene")) %>%
  ggplot(aes(value, color=gene)) + geom_histogram() +
  facet_wrap(gene~.)
int.pbmc@scale.data$rna[,
                         smp_genes] %>%
  melt(varnames=c("cell", "gene")) %>%
  ggplot(aes(value, color=gene)) + geom_histogram() +
  facet_wrap(gene~.)

  

Visualize dataset-specific VS common latent factors

Even in dataset specific component there is separation of cell types (Meaningful information that is ATAC specific?)

atac.comp <- int.pbmc@H$atac[smp,] %*% int.pbmc@V$atac

Error: object 'smp' not found

rna.comp <- int.pbmc@H$rna[smp,] %*% int.pbmc@V$rna
rna.common.comp <- int.pbmc@H$rna[smp,] %*% int.pbmc@W
tsne.rna.comp <- Rtsne(rna.comp, pca=F, check_duplicates = F, theta=0.5, perplexity=30)
rownames(tsne.rna.comp$Y) <- rownames(rna.comp)
tsne.rna.common.comp <- Rtsne(rna.common.comp, pca=F, check_duplicates = F, theta=0.5, perplexity=30)
rownames(tsne.rna.common.comp$Y) <- rownames(rna.common.comp)
tsne.rna.comp$Y %>%
  as.tibble(rownames="cell") %>%
  mutate(clust=rna_clusts[cell]) %>%
  ggplot(aes(V1, V2, color=clust)) + 
  geom_point()

tsne.rna.common.comp$Y %>%
  as.tibble(rownames="cell") %>%
  mutate(clust=rna_clusts[cell]) %>%
  ggplot(aes(V1, V2, color=clust)) + 
  geom_point()

Compare weights of dataset specific and common factors